{"cells": [{"cell_type": "markdown", "metadata": {"deletable": true, "editable": true}, "source": ["# Benchmark\n", "\n", "Ce notebook compare diff\u00e9rents mod\u00e8les depuis un notebook."]}, {"cell_type": "code", "execution_count": 1, "metadata": {"collapsed": false, "deletable": true, "editable": true}, "outputs": [{"data": {"text/html": ["<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n", "<script>\n", "function repeat_indent_string(n){\n", "    var a = \"\" ;\n", "    for ( ; n > 0 ; --n) {\n", "        a += \"    \";\n", "    }\n", "    return a;\n", "}\n", "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item) {\n", "    var anchors = document.getElementsByClassName(\"section\");\n", "    if (anchors.length == 0) {\n", "        anchors = document.getElementsByClassName(\"text_cell_render rendered_html\");\n", "    }\n", "    var i,t;\n", "    var text_menu = begin;\n", "    var text_memo = \"<pre>\\nlength:\" + anchors.length + \"\\n\";\n", "    var ind = \"\";\n", "    var memo_level = 1;\n", "    var href;\n", "    var tags = [];\n", "    var main_item = 0;\n", "    for (i = 0; i <= llast; i++) {\n", "        tags.push(\"h\" + i);\n", "    }\n", "\n", "    for (i = 0; i < anchors.length; i++) {\n", "        text_memo += \"**\" + anchors[i].id + \"--\\n\";\n", "\n", "        var child = null;\n", "        for(t = 0; t < tags.length; t++) {\n", "            var r = anchors[i].getElementsByTagName(tags[t]);\n", "            if (r.length > 0) {\n", "child = r[0];\n", "break;\n", "            }\n", "        }\n", "        if (child == null){\n", "            text_memo += \"null\\n\";\n", "            continue;\n", "        }\n", "        if (anchors[i].hasAttribute(\"id\")) {\n", "            // when converted in RST\n", "            href = anchors[i].id;\n", "            text_memo += \"#1-\" + href;\n", "            // passer \u00e0 child suivant (le chercher)\n", "        }\n", "        else if (child.hasAttribute(\"id\")) {\n", "            // in a notebook\n", "            href = child.id;\n", "            text_memo += \"#2-\" + href;\n", "        }\n", "        else {\n", "            text_memo += \"#3-\" + \"*\" + \"\\n\";\n", "            continue;\n", "        }\n", "        var title = child.textContent;\n", "        var level = parseInt(child.tagName.substring(1,2));\n", "\n", "        text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n", "\n", "        if ((level < lfirst) || (level > llast)) {\n", "            continue ;\n", "        }\n", "        if (title.endsWith('\u00b6')) {\n", "            title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\").replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\")\n", "        }\n", "\n", "        if (title.length == 0) {\n", "            continue;\n", "        }\n", "\n", "        while (level < memo_level) {\n", "            text_menu += \"</ul>\\n\";\n", "            memo_level -= 1;\n", "        }\n", "        if (level == lfirst) {\n", "            main_item += 1;\n", "        }\n", "        if (keep_item != -1 && main_item != keep_item + 1) {\n", "            // alert(main_item + \" - \" + level + \" - \" + keep_item);\n", "            continue;\n", "        }\n", "        while (level > memo_level) {\n", "            text_menu += \"<ul>\\n\";\n", "            memo_level += 1;\n", "        }\n", "        text_menu += repeat_indent_string(level-2) + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n", "    }\n", "    while (1 < memo_level) {\n", "        text_menu += \"</ul>\\n\";\n", "        memo_level -= 1;\n", "    }\n", "    text_menu += send;\n", "    //text_menu += \"\\n\" + text_memo;\n", "    return text_menu;\n", "};\n", "var update_menu = function() {\n", "    var sbegin = \"\";\n", "    var sformat = '<li><a href=\"#__HREF__\">__TITLE__</a></li>';\n", "    var send = \"\";\n", "    var keep_item = -1;\n", "    var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item);\n", "    var menu = document.getElementById(\"my_id_menu_nb\");\n", "    menu.innerHTML=text_menu;\n", "};\n", "window.setTimeout(update_menu,2000);\n", "            </script>"], "text/plain": ["<IPython.core.display.HTML object>"]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}], "source": ["from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()"]}, {"cell_type": "markdown", "metadata": {"deletable": true, "editable": true}, "source": ["Si le message *Widget Javascript not detected. It may not be installed or enabled properly.* appara\u00eet, vous devriez ex\u00e9cuter la commande ``jupyter nbextension enable --py --sys-prefix widgetsnbextension`` depuis la ligne de commande. Le code suivant vous permet de v\u00e9rifier que cela a \u00e9t\u00e9 fait."]}, {"cell_type": "code", "execution_count": 2, "metadata": {"collapsed": false, "deletable": true, "editable": true}, "outputs": [{"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "a4f30591a01b4404ad63f2e1507bd5f3"}}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "072625ddefdc4d6e963fc6c703cb9910"}}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "92eef01d3a614aa284dea6cb591e2d85"}}, "metadata": {}, "output_type": "display_data"}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "37fe02c008ce4cfc9249e5abdf74483a"}}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["\n"]}], "source": ["from tqdm import tnrange, tqdm_notebook\n", "from time import sleep\n", "\n", "for i in tnrange(3, desc='1st loop'):\n", "    for j in tqdm_notebook(range(20), desc='2nd loop'):\n", "        sleep(0.01)"]}, {"cell_type": "code", "execution_count": 3, "metadata": {"collapsed": true, "deletable": true, "editable": true}, "outputs": [], "source": ["%matplotlib inline"]}, {"cell_type": "markdown", "metadata": {"deletable": true, "editable": true}, "source": ["## Petit bench sur le clustering"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### D\u00e9finition du bench"]}, {"cell_type": "code", "execution_count": 4, "metadata": {"collapsed": false, "deletable": true, "editable": true}, "outputs": [], "source": ["import dill\n", "from tqdm import tnrange\n", "from sklearn.cluster import AgglomerativeClustering, KMeans\n", "from sklearn.datasets import make_blobs\n", "from mlstatpy.ml import MlGridBenchMark\n", "\n", "params = [dict(model=lambda : KMeans(n_clusters=3), name=\"KMeans-3\", shortname=\"km-3\"),\n", "          dict(model=lambda : AgglomerativeClustering(), name=\"AgglomerativeClustering\", shortname=\"aggclus\")]\n", "\n", "datasets = [dict(X=make_blobs(100, centers=3)[0], Nclus=3,\n", "                 name=\"blob-100-3\", shortname=\"b-100-3\", no_split=True),\n", "            dict(X=make_blobs(100, centers=5)[0], Nclus=5, \n", "                 name=\"blob-100-5\", shortname=\"b-100-5\", no_split=True) ]\n", "\n", "bench = MlGridBenchMark(\"TestName\", datasets, fLOG=None, clog=None,\n", "                        cache_file=\"cache.pickle\", pickle_module=dill, \n", "                        repetition=3, progressbar=tnrange,\n", "                        graphx=[\"_time\", \"time_train\", \"Nclus\"],\n", "                        graphy=[\"silhouette\", \"Nrows\"])"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Lancer le bench"]}, {"cell_type": "code", "execution_count": 5, "metadata": {"collapsed": false, "deletable": true, "editable": true, "scrolled": false}, "outputs": [{"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "f658c02f930b4b9099a601652a7807a7"}}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["0/|/2017-03-19 20:11:11 [BenchMark.run] number of cached run: 4:   0%|| 0/4 [00:00<?, ?it/s]                           "]}, {"data": {"application/vnd.jupyter.widget-view+json": {"model_id": "597c15ddd6564fc4bc8eef27d102ff7c"}}, "metadata": {}, "output_type": "display_data"}, {"name": "stdout", "output_type": "stream", "text": ["3/|/2017-03-19 20:11:13 [BenchMark.run] done.:  75%|| 3/4 [00:02<00:00,  1.10it/s]                                     11it/s]_train': 0.02142968022685221, 'time_test': 0.0025012412126208527, '_btry': 'aggclus-b-100-5', '_iexp': 2, 'model_name': 'AgglomerativeClustering', 'ds_name': 'blob-100-5', 'Nrows': 100, 'Nfeat': 2, 'Nclus': 5, 'no_split': True, '_date': datetime.datetime(2017, 3, 19, 20, 11, 11, 647355), '_time': 0.1007650830318858, '_span': datetime.timedelta(0, 0, 112581), '_i': 3, '_name': 'TestName'}:  75%|| 3/4 [00:00<00:00,  4.22it/s]]0:00,  3.53it/s]]\n"]}], "source": ["bench.run(params)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### R\u00e9cup\u00e9rer les r\u00e9sultats"]}, {"cell_type": "code", "execution_count": 6, "metadata": {"collapsed": false, "deletable": true, "editable": true}, "outputs": [{"data": {"text/html": ["<div>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>_btry</th>\n", "      <th>_date</th>\n", "      <th>_i</th>\n", "      <th>_iexp</th>\n", "      <th>_name</th>\n", "      <th>_span</th>\n", "      <th>_time</th>\n", "      <th>Nclus</th>\n", "      <th>Nfeat</th>\n", "      <th>Nrows</th>\n", "      <th>ds_name</th>\n", "      <th>model_name</th>\n", "      <th>no_split</th>\n", "      <th>own_score</th>\n", "      <th>silhouette</th>\n", "      <th>time_preproc</th>\n", "      <th>time_test</th>\n", "      <th>time_train</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>km-3-b-100-3</td>\n", "      <td>2017-03-19 20:11:11.132135</td>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>TestName</td>\n", "      <td>0:00:00.147610</td>\n", "      <td>0.147594</td>\n", "      <td>3</td>\n", "      <td>2</td>\n", "      <td>100</td>\n", "      <td>blob-100-3</td>\n", "      <td>KMeans-3</td>\n", "      <td>True</td>\n", "      <td>-175.396944</td>\n", "      <td>0.700618</td>\n", "      <td>0.009154</td>\n", "      <td>0.003195</td>\n", "      <td>0.044693</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>km-3-b-100-3</td>\n", "      <td>0:00:00.147610</td>\n", "      <td>0</td>\n", "      <td>1</td>\n", "      <td>TestName</td>\n", "      <td>2017-03-19 20:11:11.140141</td>\n", "      <td>0.147594</td>\n", "      <td>3</td>\n", "      <td>2</td>\n", "      <td>100</td>\n", "      <td>blob-100-3</td>\n", "      <td>KMeans-3</td>\n", "      <td>True</td>\n", "      <td>-175.396944</td>\n", "      <td>0.700618</td>\n", "      <td>0.006068</td>\n", "      <td>0.002803</td>\n", "      <td>0.037633</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>km-3-b-100-3</td>\n", "      <td>2017-03-19 20:11:11.140141</td>\n", "      <td>0</td>\n", "      <td>2</td>\n", "      <td>TestName</td>\n", "      <td>0:00:00.155620</td>\n", "      <td>0.147594</td>\n", "      <td>3</td>\n", "      <td>2</td>\n", "      <td>100</td>\n", "      <td>blob-100-3</td>\n", "      <td>KMeans-3</td>\n", "      <td>True</td>\n", "      <td>-175.396944</td>\n", "      <td>0.700618</td>\n", "      <td>0.006230</td>\n", "      <td>0.002630</td>\n", "      <td>0.035106</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>aggclus-b-100-3</td>\n", "      <td>2017-03-19 20:11:11.317283</td>\n", "      <td>1</td>\n", "      <td>0</td>\n", "      <td>TestName</td>\n", "      <td>0:00:00.096081</td>\n", "      <td>0.096700</td>\n", "      <td>3</td>\n", "      <td>2</td>\n", "      <td>100</td>\n", "      <td>blob-100-3</td>\n", "      <td>AgglomerativeClustering</td>\n", "      <td>True</td>\n", "      <td>NaN</td>\n", "      <td>0.662345</td>\n", "      <td>0.008147</td>\n", "      <td>0.002508</td>\n", "      <td>0.026997</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>aggclus-b-100-3</td>\n", "      <td>0:00:00.096081</td>\n", "      <td>1</td>\n", "      <td>1</td>\n", "      <td>TestName</td>\n", "      <td>2017-03-19 20:11:11.325288</td>\n", "      <td>0.096700</td>\n", "      <td>3</td>\n", "      <td>2</td>\n", "      <td>100</td>\n", "      <td>blob-100-3</td>\n", "      <td>AgglomerativeClustering</td>\n", "      <td>True</td>\n", "      <td>NaN</td>\n", "      <td>0.662345</td>\n", "      <td>0.009511</td>\n", "      <td>0.004156</td>\n", "      <td>0.016807</td>\n", "    </tr>\n", "    <tr>\n", "      <th>5</th>\n", "      <td>aggclus-b-100-3</td>\n", "      <td>2017-03-19 20:11:11.325288</td>\n", "      <td>1</td>\n", "      <td>2</td>\n", "      <td>TestName</td>\n", "      <td>0:00:00.106088</td>\n", "      <td>0.096700</td>\n", "      <td>3</td>\n", "      <td>2</td>\n", "      <td>100</td>\n", "      <td>blob-100-3</td>\n", "      <td>AgglomerativeClustering</td>\n", "      <td>True</td>\n", "      <td>NaN</td>\n", "      <td>0.662345</td>\n", "      <td>0.007018</td>\n", "      <td>0.003252</td>\n", "      <td>0.018227</td>\n", "    </tr>\n", "    <tr>\n", "      <th>6</th>\n", "      <td>km-3-b-100-5</td>\n", "      <td>2017-03-19 20:11:11.452688</td>\n", "      <td>2</td>\n", "      <td>0</td>\n", "      <td>TestName</td>\n", "      <td>0:00:00.145130</td>\n", "      <td>0.145012</td>\n", "      <td>5</td>\n", "      <td>2</td>\n", "      <td>100</td>\n", "      <td>blob-100-5</td>\n", "      <td>KMeans-3</td>\n", "      <td>True</td>\n", "      <td>-466.829200</td>\n", "      <td>0.790511</td>\n", "      <td>0.007587</td>\n", "      <td>0.002748</td>\n", "      <td>0.033610</td>\n", "    </tr>\n", "    <tr>\n", "      <th>7</th>\n", "      <td>km-3-b-100-5</td>\n", "      <td>0:00:00.145130</td>\n", "      <td>2</td>\n", "      <td>1</td>\n", "      <td>TestName</td>\n", "      <td>2017-03-19 20:11:11.463199</td>\n", "      <td>0.145012</td>\n", "      <td>5</td>\n", "      <td>2</td>\n", "      <td>100</td>\n", "      <td>blob-100-5</td>\n", "      <td>KMeans-3</td>\n", "      <td>True</td>\n", "      <td>-466.829200</td>\n", "      <td>0.790511</td>\n", "      <td>0.007471</td>\n", "      <td>0.002278</td>\n", "      <td>0.036098</td>\n", "    </tr>\n", "    <tr>\n", "      <th>8</th>\n", "      <td>km-3-b-100-5</td>\n", "      <td>2017-03-19 20:11:11.463199</td>\n", "      <td>2</td>\n", "      <td>2</td>\n", "      <td>TestName</td>\n", "      <td>0:00:00.153136</td>\n", "      <td>0.145012</td>\n", "      <td>5</td>\n", "      <td>2</td>\n", "      <td>100</td>\n", "      <td>blob-100-5</td>\n", "      <td>KMeans-3</td>\n", "      <td>True</td>\n", "      <td>-466.829200</td>\n", "      <td>0.790511</td>\n", "      <td>0.011576</td>\n", "      <td>0.004463</td>\n", "      <td>0.039103</td>\n", "    </tr>\n", "    <tr>\n", "      <th>9</th>\n", "      <td>aggclus-b-100-5</td>\n", "      <td>2017-03-19 20:11:11.640351</td>\n", "      <td>3</td>\n", "      <td>0</td>\n", "      <td>TestName</td>\n", "      <td>0:00:00.101573</td>\n", "      <td>0.100765</td>\n", "      <td>5</td>\n", "      <td>2</td>\n", "      <td>100</td>\n", "      <td>blob-100-5</td>\n", "      <td>AgglomerativeClustering</td>\n", "      <td>True</td>\n", "      <td>NaN</td>\n", "      <td>0.636241</td>\n", "      <td>0.009483</td>\n", "      <td>0.002418</td>\n", "      <td>0.020562</td>\n", "    </tr>\n", "    <tr>\n", "      <th>10</th>\n", "      <td>aggclus-b-100-5</td>\n", "      <td>0:00:00.101573</td>\n", "      <td>3</td>\n", "      <td>1</td>\n", "      <td>TestName</td>\n", "      <td>2017-03-19 20:11:11.647355</td>\n", "      <td>0.100765</td>\n", "      <td>5</td>\n", "      <td>2</td>\n", "      <td>100</td>\n", "      <td>blob-100-5</td>\n", "      <td>AgglomerativeClustering</td>\n", "      <td>True</td>\n", "      <td>NaN</td>\n", "      <td>0.636241</td>\n", "      <td>0.011532</td>\n", "      <td>0.001634</td>\n", "      <td>0.021456</td>\n", "    </tr>\n", "    <tr>\n", "      <th>11</th>\n", "      <td>aggclus-b-100-5</td>\n", "      <td>2017-03-19 20:11:11.647355</td>\n", "      <td>3</td>\n", "      <td>2</td>\n", "      <td>TestName</td>\n", "      <td>0:00:00.112581</td>\n", "      <td>0.100765</td>\n", "      <td>5</td>\n", "      <td>2</td>\n", "      <td>100</td>\n", "      <td>blob-100-5</td>\n", "      <td>AgglomerativeClustering</td>\n", "      <td>True</td>\n", "      <td>NaN</td>\n", "      <td>0.636241</td>\n", "      <td>0.009643</td>\n", "      <td>0.002501</td>\n", "      <td>0.021430</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["              _btry                       _date  _i  _iexp     _name  \\\n", "0      km-3-b-100-3  2017-03-19 20:11:11.132135   0      0  TestName   \n", "1      km-3-b-100-3              0:00:00.147610   0      1  TestName   \n", "2      km-3-b-100-3  2017-03-19 20:11:11.140141   0      2  TestName   \n", "3   aggclus-b-100-3  2017-03-19 20:11:11.317283   1      0  TestName   \n", "4   aggclus-b-100-3              0:00:00.096081   1      1  TestName   \n", "5   aggclus-b-100-3  2017-03-19 20:11:11.325288   1      2  TestName   \n", "6      km-3-b-100-5  2017-03-19 20:11:11.452688   2      0  TestName   \n", "7      km-3-b-100-5              0:00:00.145130   2      1  TestName   \n", "8      km-3-b-100-5  2017-03-19 20:11:11.463199   2      2  TestName   \n", "9   aggclus-b-100-5  2017-03-19 20:11:11.640351   3      0  TestName   \n", "10  aggclus-b-100-5              0:00:00.101573   3      1  TestName   \n", "11  aggclus-b-100-5  2017-03-19 20:11:11.647355   3      2  TestName   \n", "\n", "                         _span     _time  Nclus  Nfeat  Nrows     ds_name  \\\n", "0               0:00:00.147610  0.147594      3      2    100  blob-100-3   \n", "1   2017-03-19 20:11:11.140141  0.147594      3      2    100  blob-100-3   \n", "2               0:00:00.155620  0.147594      3      2    100  blob-100-3   \n", "3               0:00:00.096081  0.096700      3      2    100  blob-100-3   \n", "4   2017-03-19 20:11:11.325288  0.096700      3      2    100  blob-100-3   \n", "5               0:00:00.106088  0.096700      3      2    100  blob-100-3   \n", "6               0:00:00.145130  0.145012      5      2    100  blob-100-5   \n", "7   2017-03-19 20:11:11.463199  0.145012      5      2    100  blob-100-5   \n", "8               0:00:00.153136  0.145012      5      2    100  blob-100-5   \n", "9               0:00:00.101573  0.100765      5      2    100  blob-100-5   \n", "10  2017-03-19 20:11:11.647355  0.100765      5      2    100  blob-100-5   \n", "11              0:00:00.112581  0.100765      5      2    100  blob-100-5   \n", "\n", "                 model_name no_split   own_score  silhouette  time_preproc  \\\n", "0                  KMeans-3     True -175.396944    0.700618      0.009154   \n", "1                  KMeans-3     True -175.396944    0.700618      0.006068   \n", "2                  KMeans-3     True -175.396944    0.700618      0.006230   \n", "3   AgglomerativeClustering     True         NaN    0.662345      0.008147   \n", "4   AgglomerativeClustering     True         NaN    0.662345      0.009511   \n", "5   AgglomerativeClustering     True         NaN    0.662345      0.007018   \n", "6                  KMeans-3     True -466.829200    0.790511      0.007587   \n", "7                  KMeans-3     True -466.829200    0.790511      0.007471   \n", "8                  KMeans-3     True -466.829200    0.790511      0.011576   \n", "9   AgglomerativeClustering     True         NaN    0.636241      0.009483   \n", "10  AgglomerativeClustering     True         NaN    0.636241      0.011532   \n", "11  AgglomerativeClustering     True         NaN    0.636241      0.009643   \n", "\n", "    time_test  time_train  \n", "0    0.003195    0.044693  \n", "1    0.002803    0.037633  \n", "2    0.002630    0.035106  \n", "3    0.002508    0.026997  \n", "4    0.004156    0.016807  \n", "5    0.003252    0.018227  \n", "6    0.002748    0.033610  \n", "7    0.002278    0.036098  \n", "8    0.004463    0.039103  \n", "9    0.002418    0.020562  \n", "10   0.001634    0.021456  \n", "11   0.002501    0.021430  "]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["df = bench.to_df()\n", "df"]}, {"cell_type": "code", "execution_count": 7, "metadata": {"collapsed": false, "deletable": true, "editable": true, "scrolled": false}, "outputs": [{"data": {"text/plain": ["<matplotlib.axes._subplots.AxesSubplot at 0x122b8004748>"]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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QmJlZLg4SMzPLxUFiZma5/H8LOXVwOGbWXgAAAABJRU5ErkJggg==\n", "text/plain": ["<matplotlib.figure.Figure at 0x122b7830550>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["df.plot(x=\"time_train\", y=\"silhouette\", kind=\"scatter\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Dessin, Graphs"]}, {"cell_type": "code", "execution_count": 8, "metadata": {"collapsed": false, "deletable": true, "editable": true}, "outputs": [{"data": {"text/plain": ["array([[<matplotlib.axes._subplots.AxesSubplot object at 0x00000122B8269A90>,\n", "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000122B82E1DA0>],\n", "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000122B83512E8>,\n", "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000122B83A1828>],\n", "       [<matplotlib.axes._subplots.AxesSubplot object at 0x00000122B8409D68>,\n", "        <matplotlib.axes._subplots.AxesSubplot object at 0x00000122B8462588>]], dtype=object)"]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}, {"data": {"image/png": 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6mx06dIh+/fqdcz2lpaUMHjyYuLg4YmNjmTlzptN8GzdubHQOU0fHjx9n1KhR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pAAAg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\n3/uwrcYYY4wxxtQbPgvS3T3gM4AVwA7gXVXdLiIPicgYd7YVQKaIfA98A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"text/plain": ["<matplotlib.figure.Figure at 0x122b8289e48>"]}, "metadata": {}, "output_type": "display_data"}], "source": ["bench.plot_graphs(figsize=(12,12))"]}, {"cell_type": "code", "execution_count": 9, "metadata": {"collapsed": true}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.0"}}, "nbformat": 4, "nbformat_minor": 2}